I Need Data - For Research

Overview

Syllabus

• Introduction to Data for Research
Importance of quality data in AI
Overview of data requirements for AI applications
Data in the context of security and other fields
• Challenges in Data Acquisition
Identifying data sources
Ethical and legal considerations
Privacy concerns and regulations
• Data Acquisition Methods
Data scraping and web harvesting
API integration for data access
Utilization of public datasets
Crowdsourcing and data collection from users
• Data Quality Assessment
Criteria for high-quality data
Techniques for data validation and cleaning
Handling missing and inconsistent data
• Tools and Technologies for Data Management
Overview of data management systems
Introduction to data warehousing and data lakes
Utilization of open-source tools for managing datasets
• Case Studies
Applications in security
Applications in healthcare
Applications in finance
• Hands-on Projects
Designing a data acquisition strategy for a specific AI application
Performing a data quality assessment and cleaning
Building a small-scale data-centric AI model using acquired data
• Conclusion and Future Directions
Emerging trends in data acquisition
The evolving landscape of data privacy and security
Future technologies and methodologies in data-driven research

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Subjects

Conference Talks